Today many researcher are conducting research on autonomous driving, and autonomous driving has been rapidly developed accordingly. One of the core technologies of autonomous driving is an autonomous driving model, such as an autonomous driving algorithm, configured to make decisions about behavior of a vehicle depending on surrounding traffic environment. To improve the autonomous driving model for safer and more efficient driving, typically machine learning may be employed. According to the machine learning, test autonomous driving is carried out in a real or virtual traffic environment, using the autonomous driving model to be tested, and the autonomous driving model is improved according to a machine learning algorithm, based on the test result. The test autonomous driving in the virtual traffic environment may be advantageous in cost, public safety, and time efficiency, compared to the test autonomous driving in the real traffic environment. However, the virtual traffic environment according to the current technology may not be identical to the real traffic environment, and improvement of the virtual traffic environment to be closer to the real traffic environment may be required to reflect real environmental conditions.
These and other issues are addressed, resolved, and/or reduced using techniques described herein. The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the relevant art upon a reading of the specification and a study of the drawings.